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Learning Transferable Architectures for Scalable Image Recognition
TLDR
This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. Expand
Neural Architecture Search with Reinforcement Learning
TLDR
This paper uses a recurrent network to generate the model descriptions of neural networks and trains this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. Expand
Efficient Neural Architecture Search via Parameter Sharing
TLDR
Efficient Neural Architecture Search is a fast and inexpensive approach for automatic model design that establishes a new state-of-the-art among all methods without post-training processing and delivers strong empirical performances using much fewer GPU-hours. Expand
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
TLDR
This work presents SpecAugment, a simple data augmentation method for speech recognition that is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients) and achieves state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work. Expand
Progressive Neural Architecture Search
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionaryExpand
AutoAugment: Learning Augmentation Policies from Data
TLDR
This paper describes a simple procedure called AutoAugment to automatically search for improved data augmentation policies, which achieves state-of-the-art accuracy on CIFAR-10, CIFar-100, SVHN, and ImageNet (without additional data). Expand
Searching for Activation Functions
TLDR
The experiments show that the best discovered activation function, f(x) = x \cdot \text{sigmoid}(\beta x)$, which is named Swish, tends to work better than ReLU on deeper models across a number of challenging datasets. Expand
AutoAugment: Learning Augmentation Strategies From Data
TLDR
This paper describes a simple procedure called AutoAugment to automatically search for improved data augmentation policies, which achieves state-of-the-art accuracy on CIFAR-10, CIFar-100, SVHN, and ImageNet (without additional data). Expand
Randaugment: Practical automated data augmentation with a reduced search space
TLDR
This work proposes a simplified search space that vastly reduces the computational expense of automated augmentation, and permits the removal of a separate proxy task. Expand
Understanding and Simplifying One-Shot Architecture Search
TLDR
With careful experimental analysis, it is shown that it is possible to efficiently identify promising architectures from a complex search space without either hypernetworks or reinforcement learning controllers. Expand
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